An explainable deep learning system for automated ECG arrhythmia detection using a hybrid 1D CNN–LSTM model with Grad-CAM–based clinical interpretability.
Heartbeat arrhythmia classification with a neural network written from scratch in NumPy — forward/backward propagation, optimisers and regularisation by hand, verified against PyTorch to 1e-17. Patient-disjoint evaluation on MIT-BIH, 216 logged experiments, and a documented model-selection failure.
Real-time ECG arrhythmia classification on STM32F446RE using a 1-D CNN and ST X-CUBE-AI — trained on MIT-BIH, 98.14% accuracy, 460 KiB flash footprint
This repository contains the fMRI analysis code accompanying the manuscript "Dynamic cortical responses to premature contractions of the heart in humans" (Reinfeld, Greschke et. al). It includes multiple steps of preprocessing, analysing and visualising fMRI BOLD data in order to explore cortical responses to cardiac premature contractions.
Deep Learning-based ECG Arrhythmia Classification using CNN + BiLSTM to detect cardiac patterns with performance analysis and interactive Streamlit visualization.
ECG delineation using Stationary Wavelet Transform (SWT) and multi-label cardiac arrhythmia detection using a Deep Residual ANN.
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